DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 26 March 2026 have been fully considered but they are not persuasive.
Claims 1-20 are pending in this application and have been considered below.
Argument:
The applicant argues an "accelerator unit (AU) configured to" recites a feature a person of ordinary skill in the art would understand has a sufficient structure to execute tasks. For example, applicant states a person of ordinary skill in the art would understand “an accelerator unit (AU) configured to” to have a sufficiently definite meaning, for example, "a processor for accelerating a specific function or workload." Thus applicant argues that the Federal Circuit has held that "the term 'digital processing unit' is not a 'means-plus-function' limitation subject to analysis under section 112, paragraph 6."
Response:
The Examiner states that in light of MPEP 2111, the Examiner has interpreted the claims properly. Specifically, during patent prosecution, the pending claims must be “given their broadest reasonable interpretation assistant with the specification.” The Examiner has interpreted the claim language in reference to the specification, via an interpretation under 35 USC 112(f). Because applicant has the opportunity to amend the claims during prosecution, given a claim in its broadest reasonable interpretation will reduce the possibility that the claim, once issued will be interpreted more or broadly than is justified.
Argument:
The applicant argues that Pottorff does not disclose or suggest assigning weights to motion vectors of a frame that are estimated to intersect a same location (e.g., first location) of an interpolated frame. Applicant states Pottorff is directed to "one or more neural networks to perform video processing operations including operations to increase a frame rate of a video." (Pottorff, [0077].) Applicant then notes that Pottorff discloses generating a plurality of interpolated frames between a current frame and previous frame. Further, applicant notes that Pottorff discloses that a neural network generates blending factors indicating how to combine these interpolated frames, with such blending factors including vectors indicating weights to apply to a pixel value at a certain location within an interpolated frame. Applicant then alleges that Pottorff does not disclose or suggest assigning a weight to each motion of a vector of a plurality of motion vectors estimated to intersect a same location within an interpolated frame.
Response:
US Patent Publication 2024 0098216 A1, (Pottorff et al.) shows the limitation “assigning a weight to each motion vector of a plurality of motion vectors of a current frame that are estimated to intersect a first location of an interpolated frame between the current frame and a previous frame ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085]).”
Argument:
The applicant argues that Pottorff discloses that "neural network 110 generates one or more blending factors 112 based, at least in part, on a set of motion vectors corresponding to pixels of...[a] current frame 106," Applicant argues that Pottorff does not suggest that the neural network includes applying weights to motion vectors each predicted to intersect the same point of an interpolated frame. Applicant thus argues that Pottorff does not disclose or suggest "assigning a weight to each motion vector of a plurality of motion vectors of a current frame that are estimated to intersect a first location of an interpolated frame between the current frame and a previous frame" as recited by claim 1.
Response:
US Patent Publication 2024 0098216 A1, (Pottorff et al.) shows the limitation “assigning a weight to each motion vector of a plurality of motion vectors of a current frame that are estimated to intersect a first location of an interpolated frame between the current frame and a previous frame ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085]).” A pixel is a point of a frame. In addition, Pottorff states “at least one embodiment, for example, blending factors have a resolution of 1080p and there is a separate blending factor for each pixel in a 1080p image,” paragraph [0082] and “said processor or other processor performs upsampling of one or more arrays of blending factors by establishing a correspondence between pixel locations according to said up sampled resolution and blending factors, where the correspondence can apply a single blending factor to multiple pixels,” paragraph [0083].
In response to applicant’s argument that “Pottorff does not suggest that the neural network includes applying weights to motion vectors each predicted to intersect the same point of an interpolated frame,” the test for obviousness is not whether the features of a reference may be bodily incorporated into the structure of the claims; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Argument:
The applicant argues that Claim 1 further recites "selecting a first motion vector from the plurality of motion vectors based on the weight of the first motion vector to generate a motion vector field for the interpolated frame." Applicant then argues that Pottorff does not disclose or suggest assigning weights to motion vectors each predicted to intersect the same point of an interpolated frame. Applicant states that Pottorff contains no discussion about selecting a motion vector based on these weights or generating a motion vector field from the selected motion vector.
Response:
US Patent Publication 2024 0098216 A1, (Pottorff et al.) shows the limitation “selecting a first motion vector from the plurality of motion vectors based on the weight of the first motion vector to generate a motion vector field for the interpolated frame ("store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic," paragraph [0191])”. In addition, see the citation to the limitation above, “assigning a weight to each motion vector of a plurality of motion vectors of a current frame that are estimated to intersect a first location of an interpolated frame between the current frame and a previous frame ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085])” where an array is a motion vector field.
In response to applicant’s argument that “Pottorff contains no discussion about selecting a motion vector based on these weights or generating a motion vector field from the selected motion vector,” the test for obviousness is not whether the features of a reference may be bodily incorporated into the structure of the claims; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Argument:
The applicant argues that claim 2 recites "the weight is based on a distance of the first motion vector from a virtual camera position of the interpolated frame" Applicant states that Pottorff contains no discussion as to generating any weights based on a distance of motion vector from a camera position. Further, applicant states that Pottorff discloses that the blending factors of a pixel may blend certain percentages of the motion from different intermediate frame, which such percentages between based on a type of motion, such as being from a game engine. Applicant then concludes that Pottorff contains no discussion as to generating blending factors or any weights based on any distance between a pixel or motion vector and a camera position.
Response:
US Patent Publication 2024 0098216 A1, (Pottorff et al.) shows the limitation wherein the weight is based on a distance of the first motion vector from a virtual camera position of the interpolated frame ("movement of a pixel from frame to frame depending on a lot of different factors, such as lateral movement of objects within a scene of a video, rotational movement of objects within a scene of a video, camera motion of a virtual camera, and the like," paragraph [0112] which teaches distance from a virtual camera).
Information Disclosure Statement
The IDSs dated 23 January 2026, 27 February 2026 and 2 April 2026 have been considered and placed in the application file.
The IDSs dated 11 September 2024, 18 September 2024, 10 October 2024, 22 October 2024, 29 October 2024, 23 January 2025, 27 February 2025, 17 April 2025, 20 August 2025, 11 September 2025, 24 October 2025 and 2 December 2025 that have been previously considered remain placed in the application file.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“accelerator unit (AU) configured to” in claims 9 and 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
1st Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-5, 8-13 and 16-20 (all claims except 6-7 and 14-15) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2024 0098216 A1, (Pottorff et al.).
Claim 1
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Regarding Claim 1, Pottorff et al. teach a method ("processing resources used to interpolate video frames using one or more neural networks," paragraph [0002]) comprising:
[AltContent: textbox (Pottorff et al. Fig. 10 showing generating interpolated frames using optical flow)]assigning a weight to each motion vector of a plurality of motion vectors of a current frame that are estimated to intersect a first location of an interpolated frame between the current frame and a previous frame ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085]);
selecting a first motion vector from the plurality of motion vectors based on the weight of the first motion vector to generate a motion vector field for the interpolated frame ("store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic," paragraph [0191]); and
generating the interpolated frame based on the motion vector field ("generating one or more additional frames; and blending the intermediate video frame with the one or more additional frames," paragraph [0691]).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains to employ combinations and sub-combinations of these complementary embodiments, because XXXX et al. explicitly motivates doing so at least in paragraphs [0077], [0090] and [0723] including “Although descriptions herein set forth example implementations of described techniques, other architectures may be used to implement described functionality, and are intended to be within scope of this disclosure.” and otherwise motivating experimentation and optimization.
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of system claim 9 and system claim 17 while noting that the rejection above cites to both device and method disclosures. Claims 9 and 17 are mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Claim 2
Regarding claim 2, Pottorff et al. teach the method of claim 1, wherein the weight is based on a distance of the first motion vector from a virtual camera position of the interpolated frame ("movement of a pixel from frame to frame depending on a lot of different factors, such as lateral movement of objects within a scene of a video, rotational movement of objects within a scene of a video, camera motion of a virtual camera, and the like," paragraph [0112] which teaches distance from a virtual camera).
Claim 3
Regarding claim 3, Pottorff et al. teach the method of claim 1, further comprising:
storing each motion vector of the plurality of motion vectors and the weight assigned to each motion vector ("weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor(s) 3308 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein," paragraph [00399]), wherein
selecting the first motion vector comprises sampling the plurality of motion vectors intersecting the first location and selecting the motion vector having a greatest weight ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085] where applying a weight teaches to select the vectors in an order, highest first being an order).
Claim 4
Regarding claim 4, Pottorff et al. teach the method of claim 3, wherein storing comprises separately storing an x coordinate and a y coordinate of each motion vector ("plane equations are transmitted to a coarse raster engine to generate coverage information (e.g., an x, y coverage mask for a tile) for primitive," paragraph [0474]).
Claim 5
Regarding claim 5, Pottorff et al. teach the method of claim 4, wherein sampling comprises, for each motion vector, separately writing the x coordinate and the y coordinate to the first location in an atomic operation ("such as camera imaging pipelines, and enables use of and/or implements global memory atomics that may be shared between graphics core 3700 and CPUs within an SoC," paragraph [0432]).
Claim 8
Regarding claim 8, Pottorff et al. teach the method of claim 7, wherein replacing the invalid motion vector comprises selecting the first valid adjacent motion vector from a plurality of valid adjacent motion vectors based on the first valid adjacent motion vector being furthest of the plurality of valid adjacent motion vectors from a virtual camera position of the interpolated frame ("forward motion vectors can be calculated from reverse motion vectors, reverse motion vectors can be calculated from forward motion vectors, or optical flow vectors can be calculated using depth, camera position, and/or other such data. In at least one embodiment, after step 1006, example process 1000 continues at step 1008," paragraph [0172]).
Claim 9
Regarding claim 9, Pottorff et al. teach a processing system comprising:
an accelerator unit (AU) ("one of FPGA/ASIC chips of an accelerator is connected to a host system through a PCI-Express connection (1730)," paragraph [0225])configured to:
assign a weight to each motion vector of a plurality of motion vectors of a current frame that are estimated to intersect a first location of an interpolated frame between the current frame and a previous frame("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085]);
select a first motion vector from the plurality of motion vectors based on the weight of the first motion vector to generate a motion vector field for the interpolated frame ("store graph code or other software to control timing and/or order, in which weight and/or other parameter information is to be loaded to configure, logic," paragraph [0191]); and
generate the interpolated frame based on the motion vector field ("generating one or more additional frames; and blending the intermediate video frame with the one or more additional frames," paragraph [0691]).
Claim 10
Regarding claim 10, Pottorff et al. teach the processing system of claim 9, wherein the weight is based on a distance of the first motion vector from a virtual camera position of the interpolated frame ("movement of a pixel from frame to frame depending on a lot of different factors, such as lateral movement of objects within a scene of a video, rotational movement of objects within a scene of a video, camera motion of a virtual camera, and the like," paragraph [0112] which teaches distance from a virtual camera).
Claim 11
Regarding claim 11, Pottorff et al. teach the processing system of claim 9, wherein the AU is further configured to:
store each motion vector of the plurality of motion vectors and the weight assigned to each motion vector ("weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor(s) 3308 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein," paragraph [00399]); and
select the first motion vector by sampling the plurality of motion vectors intersecting the first location and selecting the motion vector having a greatest weight ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085] where applying a weight teaches to select the vectors in an order, highest first being an order).
Claim 12
Regarding claim 12, Pottorff et al. teach the processing system of claim 11, wherein the AU is further configured to: separately store an x coordinate and a y coordinate of each motion vector ("plane equations are transmitted to a coarse raster engine to generate coverage information (e.g., an x, y coverage mask for a tile) for primitive," paragraph [0474]).
Claim 13
Regarding claim 13, Pottorff et al. teach the processing system of claim 12, wherein the AU is further configured to: for each motion vector, separately write the x coordinate and the y coordinate to the first location in an atomic operation ("such as camera imaging pipelines, and enables use of and/or implements global memory atomics that may be shared between graphics core 3700 and CPUs within an SoC," paragraph [0432]).
Claim 16
Regarding claim 16, Pottorff et al. teach the processing system of claim 15, wherein the AU is further configured to: select the first valid adjacent motion vector from a plurality of valid adjacent motion vectors based on the first valid adjacent motion vector being furthest of the plurality of valid adjacent motion vectors from a virtual camera position of the interpolated frame ("forward motion vectors can be calculated from reverse motion vectors, reverse motion vectors can be calculated from forward motion vectors, or optical flow vectors can be calculated using depth, camera position, and/or other such data. In at least one embodiment, after step 1006, example process 1000 continues at step 1008," paragraph [0172])..
Claim 17
Regarding claim 17, Pottorff et al. teach a processing system, comprising:
an accelerator unit (AU)("one of FPGA/ASIC chips of an accelerator is connected to a host system through a PCI-Express connection (1730)," paragraph [0225]) configured to:
generate a motion vector field for an interpolated frame between a current rendered frame and a previous rendered frame based on a plurality of selected motion vectors, wherein each selected motion vector is selected from a plurality of motion vectors of the current rendered frame that are estimated to intersect a location of the interpolated frame based on a weight of each motion vector of the plurality of motion vectors ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085] where an array is a motion vector field); and
generate the interpolated frame based on the motion vector field ("generating one or more additional frames; and blending the intermediate video frame with the one or more additional frames," paragraph [0691]).
Claim 18
Regarding claim 18, Pottorff et al. teach the processing system of claim 17, wherein the weight is based at least in part on a distance of each motion vector of the plurality of motion vectors from a virtual camera position of the interpolated frame ("movement of a pixel from frame to frame depending on a lot of different factors, such as lateral movement of objects within a scene of a video, rotational movement of objects within a scene of a video, camera motion of a virtual camera, and the like," paragraph [0112] which teaches distance from a virtual camera).
Claim 19
Regarding claim 19, Pottorff et al. teach the processing system of claim 17, wherein the AU is further configured to:
store each motion vector of the plurality of motion vectors and the weight assigned to each motion vector ("weight parameters may be stored in on-chip or off-chip memory and/or registers (shown or not shown) that configure ALUs of graphics processor(s) 3308 to perform one or more machine learning algorithms, neural network architectures, use cases, or training techniques described herein," paragraph [00399]); and
select each selected motion vector by sampling the plurality of motion vectors of the current rendered frame that intersect a location of the interpolated frame and selecting the motion vector having a greatest weight ("first array comprises a plurality of three-dimensional or other dimensional vectors, where each component indicates a weight to be applied to a corresponding pixel value in a corresponding motion warped color frame," paragraph [0085] where applying a weight teaches to select the vectors in an order, highest first being an order).
Claim 20
Regarding claim 20, Pottorff et al. teach the processing system of claim 17, wherein the AU is further configured to: for each motion vector, separately write an x coordinate and a y coordinate to the location ("plane equations are transmitted to a coarse raster engine to generate coverage information (e.g., an x, y coverage mask for a tile) for primitive," paragraph [0474]) in an atomic operation ("such as camera imaging pipelines, and enables use of and/or implements global memory atomics that may be shared between graphics core 3700 and CPUs within an SoC," paragraph [0432]).
2nd Claim Rejections - 35 USC § 103
Claims 6-7 and 14-15 (all remaining claims) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2024 0098216 A1, (Pottorff et al.) in view of US Patent Publication 2023 0209087 A1, (Baijal et al.).
Claim 6
Regarding Claim 6, Pottorff et al. teach the method of claim 5, as noted above.
Pottorff et al. do not explicitly teach all of endpoint of the motion vector based on a number of interpolated frames.
[AltContent: textbox (Baijal et al. Fig. 6, showing using a mapping table with a interpolation frame generator.)]
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However, Baijal et al. teach wherein the first location is a distance from each endpoint of the motion vector based on a number of interpolated frames being generated between the current frame and the previous frame ("mapping table 1000 may include, for each parameter value, a motion vector representing a moving direction and a moving amplitude of an object mapped to each parameter value (e.g., Vl-V3 and Vl'-V3')," paragraph [0231]).
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Video Frame Blending” as taught by Pottorff et al. to use “Method and Device for Improving Video Quality” as taught by Baijal et al.
The suggestion/motivation for doing so would have been that, “FPS of the image data may differ from the FPS of an apparatus for reproducing the image data. For example, the FPS of an image display device that outputs the image data may be greater than the FPS of the image data. If this situation occurs, when received image data is transmitted as it is (e.g., no post processing), image quality deteriorates, and thus, the image display device is required to increase the FPS to improve the quality of the image.” as noted by the Baijal et al. disclosure in paragraph [0003].
Claim 7
Regarding claim 7, Pottorff et al. teach the method of claim 1, as noted above.
Pottorff et al. do not explicitly teach all of an invalid motion vector.
However, Baijal et al. teach further comprising: replacing an invalid motion vector of the plurality of motion vectors with a first valid adjacent motion vector of the plurality of motion vectors ("the interpolation frame generator 517 may learn hierarchical features by collecting context information from an adjacent pixel of the input frame. The interpolation frame generator 517 may combine a new frame through depth estimation, content extraction, kernel estimation, and frame synthesis by using the input frame and the motion information received from the motion information obtainer 511," paragraph [0167] where hierarchical features teaches invalid motion vectors, as some motion vectors are above other invalid motion vectors).
Pottorff et al. and Baijal et al. are combined as per claim 6.
Claim 14
Regarding claim 14, Pottorff et al. teach the processing system of claim 13, as noted above.
Pottorff et al. do not explicitly teach all of endpoint of the motion vector based on a number of interpolated frames.
However, Baijal et al. teach wherein the first location is a distance from each endpoint of the motion vector based on a number of interpolated frames being generated between the current frame and the previous frame ("mapping table 1000 may include, for each parameter value, a motion vector representing a moving direction and a moving amplitude of an object mapped to each parameter value (e.g., Vl-V3 and Vl'-V3')," paragraph [0231]).
Pottorff et al. and Baijal et al. are combined as per claim 6.
Claim 15
Regarding claim 15, Pottorff et al. teach the processing system of claim 9, as noted above.
Pottorff et al. do not explicitly teach all of an invalid motion vector.
However, Baijal et al. teach wherein the AU is further configured to: replace an invalid motion vector of the plurality of motion vectors with a first valid adjacent motion vector of the plurality of motion vectors ("the interpolation frame generator 517 may learn hierarchical features by collecting context information from an adjacent pixel of the input frame. The interpolation frame generator 517 may combine a new frame through depth estimation, content extraction, kernel estimation, and frame synthesis by using the input frame and the motion information received from the motion information obtainer 511," paragraph [0167] where hierarchical features teaches invalid motion vectors, as some motion vectors are above other invalid motion vectors).
Pottorff et al. and Baijal et al. are combined as per claim 6.
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2021 02798840 A1 to Chi et al. discloses a temporal pyramidal optical flow refinement module that performs coarse-to-fine refinement of the optical flow maps used to generate the intermediate frames, focusing a proportionally greater amount of refinement attention to the optical flow maps for the high-error middle frames. A temporal pyramidal pixel refinement module performs coarse-to-fine refinement of the generated intermediate frames, focusing a proportionally greater amount of refinement attention to the high-error middle frames.
US Patent Publication 2016 0371816 A1 to Choudhury et al. discloses a system for enhancing an image includes a cadence detection process that detects a cadence of a series of frames, a scene cut detection process that detects a scene cut of the series of frames, and a noise monitoring process that detects noise in the series of frames based upon the cadence detection process. The system also includes a temporal filtering process temporally filtering the series of frames wherein the temporal filtering is modified based upon the scene cut detection process and the noise monitoring process, and a spatial noise reduction process reducing spatial noise in the series of frames.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/H.E.W/Examiner, Art Unit 2664
Date: 22 April 2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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